{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T12:35:50Z","timestamp":1763728550243,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,16]],"date-time":"2023-08-16T00:00:00Z","timestamp":1692144000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62271499","61971432","62022092","2020-JCJQ-QT-011"],"award-info":[{"award-number":["62271499","61971432","62022092","2020-JCJQ-QT-011"]}]},{"name":"Young Elite Scientists Sponsorship Program by CAST","award":["62271499","61971432","62022092","2020-JCJQ-QT-011"],"award-info":[{"award-number":["62271499","61971432","62022092","2020-JCJQ-QT-011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deep-learning-based SAR ship classification has become a research hotspot in the military and civilian fields and achieved remarkable performance. However, the volume of available SAR ship classification data is relatively small, meaning that previous deep-learning-based methods have usually struggled with overfitting problems. Moreover, due to the limitation of the SAR imaging mechanism, the large intraclass diversity and small interclass similarity further degrade the classification performance. To address these issues, we propose a label smoothing auxiliary classifier generative adversarial network with triplet loss (LST-ACGAN) for SAR ship classification. In our method, an ACGAN is introduced to generate SAR ship samples with category labels. To address the model collapse problem in the ACGAN, the smooth category labels are assigned to generated samples. Moreover, triplet loss is integrated into the ACGAN for discriminative feature learning to enhance the margin of different classes. Extensive experiments on the OpenSARShip dataset demonstrate the superior performance of our method compared to the previous methods.<\/jats:p>","DOI":"10.3390\/rs15164058","type":"journal-article","created":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T10:42:29Z","timestamp":1692268949000},"page":"4058","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Label Smoothing Auxiliary Classifier Generative Adversarial Network with Triplet Loss for SAR Ship Classification"],"prefix":"10.3390","volume":"15","author":[{"given":"Congan","family":"Xu","sequence":"first","affiliation":[{"name":"Advanced Technology Research Institute, Beijing Institute of Technology, Jinan 250300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Long","family":"Gao","sequence":"additional","affiliation":[{"name":"Information Fusion Institute, Naval Aviation University, Yantai 264000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hang","family":"Su","sequence":"additional","affiliation":[{"name":"Information Fusion Institute, Naval Aviation University, Yantai 264000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianting","family":"Zhang","sequence":"additional","affiliation":[{"name":"No. 91977 Unit of People\u2019s Liberation Army of China, Beijing 100036, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junfeng","family":"Wu","sequence":"additional","affiliation":[{"name":"Information Fusion Institute, Naval Aviation University, Yantai 264000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjun","family":"Yan","sequence":"additional","affiliation":[{"name":"Information Fusion Institute, Naval Aviation University, Yantai 264000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,16]]},"reference":[{"key":"ref_1","unstructured":"Martino, G.D., Iodice, A., Riccio, D., and Ruello, G. 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